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I’m an undergraduate studying mechatronics engineering in Nigeria. Our paper, "Evaluating Mental Health LLM Responses to Localized African English", was accepted as a poster at the Global South AI Affinity Event at NeurIPS 2026 in Sydney, and I am the lead author.
My conference registration costs are already covered, but I cannot afford an international flight to Australia, lodging, a visa and meals on my current student budget. I am requesting $2,400 to cover travel expenses to present our work in person at the NeurIPS 2026 conference in Australia.
Why this research matters for AI safety: Frontier AI safety evals and automated monitors (LLM-as-a-judge) assume models generalize cleanly across various inputs. But what if safe behavior breaks down because of natural linguistic drift and the semantics look the same?
During the Apart research global south hackathon alongside my co-authors Namirah Rasul and Ewura Ama Sam, we tested MentaLLaMA-chat-7B on 300 controlled prompt variants (100 clinician-grounded scenarios from MentalChat16K held constant across Standard English, Nigerian Pidgin, and Ghanaian English).
OpenReview: https://openreview.net/forum?id=ICZEGRAVCn
Apart Hackathon: https://apartresearch.com/sprints/projects/evaluating-mental-health-llm-responses-to-localized-african-english-pzag
The core research is finished and accepted. The goal here is to get to Sydney so I can present the findings, gather feedback, and turn this into broader safety benchmark work:
Present the poster at NeurIPS: Share our results directly with researchers focused on out-of-distribution robustness, scalable oversight, and automated red-teaming.
Stress-test LLM-as-a-judge rubrics: Discuss methods with evaluation researchers on how to design automated evaluators that detect tail degradation instead of averaging it away.
Scale to frontier safety benchmarks: I want to adapt this controlled semantic-invariance setup into an open-source evaluation suite (compatible with platforms like UK AISI’s Inspect AI) to test whether frontier models exhibit sandbagging or alignment breakdown under dialectal shifts.
Support fieldbuilding: As co-founder and research lead of AI Safety Hub Nigeria, attending in person allows me to bring back technical connections, open problems, and collaboration opportunities to our local fellows.
My conference registration is already covered. All requested funds go directly toward necessary travel costs:
Round-trip flight (Nigeria – Sydney): $1,500
Accommodation (5 nights in Sydney): $650
Visa: $150
Meals $100
Total Requested: $2,400
Minimum funding goal ($1,650): $1,650 covers my flight and visa, so I can physically reach Sydney. If I only hit the minimum, I will try my best to cover food, transit, and shared-budget hostel lodging out of pocket. If any pending grants come through, I will reduce this goal immediately.
I am the first author, with co-authors Namirah Rasul and Ewura Ama Sam. I am a mechatronics engineering student at Ahmadu Bello University, Nigeria.
For the last three years, I’ve been focused on empirical LLM evaluations and building technical AI safety capacity:
Evaluations & Research: Built SandbagBench-Lite (a black-box harness for measuring strategic underperformance in LLMs) and worked as an AI Safety & Evaluation Research Intern at HeyLina (designing conversational evaluation rubrics and LLM-as-a-judge frameworks). Authored Forecasting AGI: A Granular, CHC-Based Approach and received recognition in the ENAIS AI Safety Action Competition for research on behavioral adaptation (Punctuation Paranoia).
Fellowships: 2024 Non-Trivial Fellow, Cooperative AI Research Fellowship finalist, and alumnus of the TReND-CaMinA ML & Computational Neuroscience program in Kenya.
Community Leadership: Co-founder and Technical Lead of AI Safety Hub Nigeria, President of Effective Altruism ABU, and Policy Lead for PauseAI Nigeria.
On my current student budget, I can't afford the flight to Australia without travel funding. Hence, not attending the conference means I miss the opportunity to present the poster in person, get technical feedback from leading AI evals researchers, and build collaborations.
$0 has been raised to date.